KNIME Server Installation Guide
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Text Mining Course for KNIME Analytics Platform
Text Mining Course for KNIME Analytics Platform KNIME AG Copyright © 2018 KNIME AG Table of Contents 1. The Open Analytics Platform 2. The Text Processing Extension 3. Importing Text 4. Enrichment 5. Preprocessing 6. Transformation 7. Classification 8. Visualization 9. Clustering 10. Supplementary Workflows Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 2 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ Overview KNIME Analytics Platform Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 3 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations, such as Java, R, Python, Weka, H2O, etc. Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 4 Noncommercial-Share Alike license 2 https://creativecommons.org/licenses/by-nc-sa/4.0/ Visual KNIME Workflows NODES perform tasks on data Not Configured Configured Outputs Inputs Executed Status Error Nodes are combined to create WORKFLOWS Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 5 Noncommercial-Share Alike license 3 https://creativecommons.org/licenses/by-nc-sa/4.0/ Data Access • Databases • MySQL, MS SQL Server, PostgreSQL • any JDBC (Oracle, DB2, …) • Files • CSV, txt -
Automated Acceptance Testing on Macos – a Survey Study Focused On
ASSIGNMENT OF BACHELOR’S THESIS Title: Automated Acceptance Testing on macOS – A survey study focused on Avast Passwords for mac Student: David Mokoš Supervisor: MSc Felix Javier Acero Salazar Study Programme: Informatics Study Branch: Web and Software Engineering Department: Department of Software Engineering Validity: Until the end of summer semester 2018/19 Instructions 1] Survey the different technologies available for writing acceptance tests on macOS. 2] Select an appropriate test stack: GUI driving technology, testing framework, etc. 3] Prepare application for acceptance testing based on the selected test stack. 4] Implement a suite of acceptance tests to cover the most relevant requirements of Avast Passwords for mac. References Will be provided by the supervisor. Ing. Michal Valenta, Ph.D. doc. RNDr. Ing. Marcel Jiřina, Ph.D. Head of Department Dean Prague November 28, 2017 Czech Technical University in Prague Faculty of Information Technology Department of Software Engineering Bachelor’s thesis Automated Acceptance Testing on macOS – A survey study focused on Avast Passwords for Mac David Mokoˇs Supervisor: MSc. Felix Javier Acero Salazar May 14, 2018 Acknowledgements First and foremost, I would like to thank my thesis supervisor, MSc. Felix Javier Acero Salazar, for his patient guidance, advice and encouragement he has provided throughout the whole time I have been working in Avast. I have been lucky to meet such a hardworking, inspirative and helpful person who cares so much about my work and who always keeps his word. His curiosity and positive mindset inspired me and helped me particularly when exploring new ideas. His careful revising contributed hugely to the creation of the thesis. -
Imagej2-Allow the Users to Use Directly Use/Update Imagej2 Plugins Inside KNIME As Well As Recording and Running KNIME Workflows in Imagej2
The KNIME Image Processing Extension for Biomedical Image Analysis Andries Zijlstra (Vanderbilt University Medical Center The need for image processing in medicine Kevin Eliceiri (University of Wisconsin-Madison) KNIME Image Processing and ImageJ Ecosystem [email protected] [email protected] The need for precision oncology 36% of newly diagnosed cancers, and 10% of all cancer deaths in men Out of every 100 men... 16 will be diagnosed with prostate cancer in their lifetime In reality, up to 80 will have prostate cancer by age 70 And 3 will die from it. But which 3 ? In the meantime, we The goal: Diagnose patients that have over-treat many aggressive disease through Precision Medicine patients Objectives of Approach to Modern Medicine Precision Medicine • Measure many things (data density) • Improved outcome through • Make very accurate measurements (fidelity) personalized/precision medicine • Consider multiple perspectives (differential) • Reduced expense/resource allocation through • Achieve confidence in the diagnosis improved diagnosis, prognosis, treatment • Match patients with a treatment they are most • Maximize quality of life by “targeted” therapy likely to respond to. Objectives of Approach to Modern Medicine Precision Medicine • Measure many things (data density) • Improved outcome through • Make very accurate measurements (fidelity) personalized/precision medicine • Consider multiple perspectives (differential) • Reduced expense/resource allocation through • Achieve confidence in the diagnosis improved diagnosis, -
KNIME Workbench Guide
KNIME Workbench Guide KNIME AG, Zurich, Switzerland Version 4.4 (last updated on 2021-06-08) Table of Contents Workspaces . 1 KNIME Workbench . 2 Welcome page . 4 Workflow editor & nodes . 5 KNIME Explorer . 13 Workflow Coach . 35 Node repository . 37 KNIME Hub view . 38 Description. 40 Node Monitor. 40 Outline. 41 Console. 41 Customizing the KNIME Workbench . 42 Reset and logging . 42 Show heap status . 42 Configuring KNIME Analytics Platform . 43 Preferences . 43 Setting up knime.ini. 47 KNIME runtime options . 49 KNIME tables . 55 Data table . 55 Column types. 56 Sorting . 59 Column rendering . 59 Table storage. 61 KNIME Workbench Guide This guide describes the first steps to take after starting KNIME Analytics Platform and points you to the resources available in the KNIME Workbench for building workflows. It also explains how to customize the workbench and configure KNIME Analytics Platform to best suit specific needs. In the last part of this guide we introduce data tables. Workspaces When you start KNIME Analytics Platform, the KNIME Analytics Platform launcher window appears and you are asked to define the KNIME workspace, as shown in Figure 1. The KNIME workspace is a folder on the local computer to store KNIME workflows, node settings, and data produced by the workflow. Figure 1. KNIME Analytics Platform launcher The workflows and data stored in the workspace are available through the KNIME Explorer in the upper left corner of the KNIME Workbench. © 2021 KNIME AG. All rights reserved. 1 KNIME Workbench Guide KNIME Workbench After selecting a workspace for the current project, click Launch. The KNIME Analytics Platform user interface - the KNIME Workbench - opens. -
Data Analytics with Knime
DATA ANALYTICS WITH KNIME v.3.4.0 QUALIFICATIONS & EXPERIENCE ▶ 38 years of providing professional services to state and local taxing officials ▶ TMA works exclusively with government partners WHO ▶ TMA is composed of 150+ WE ARE employees in five main offices across the United States Tax Management Associates is a professional services firm that has ▶ Our main focus is on revenue served the interests of state and local enhancement services for state government since 1979. and local jurisdictions and property tax compliance efforts KNIME POWERED CUSTOM ANALYTICS ▶ TMA is a proud KNIME Trusted Consulting Partner. Visit: www.knime.org/knime-trusted-partners ▶ Successful analytics solutions: ○ Fraud Detection (Michigan Department of Treasury) ○ Entity Discovery (multiple counties) ○ Data Aggregation (Louisiana State Tax Commission) KNIME POWERED CUSTOM ANALYTICS ▶ KNIME is an open source data toolkit ▶ Active development community and core team ▶ GUI based with scripting integration ○ Easy adoption, integration, and training ▶ Data ingestion, transformation, analytics, and reporting FEATURES & TERMINOLOGY KNIME WORKBENCH TAX MANAGEMENT ASSOCIATES, INC. KNIME WORKFLOW TAX MANAGEMENT ASSOCIATES, INC. KNIME NODES TAX MANAGEMENT ASSOCIATES, INC. DATA TYPES & SOURCES DATA AGNOSTIC ▶ Flat Files ▶ Shapefiles ▶ Xls/x Reader ▶ HTTP Requests ▶ Fixed Width ▶ RSS Feeds ▶ Text Files ▶ Custom API’s/Curl ▶ Image Files ▶ Standard API’s ▶ XML ▶ JSON TAX MANAGEMENT ASSOCIATES, INC. KNIME DATA NODES TAX MANAGEMENT ASSOCIATES, INC. DATABASE AGNOSTIC ▶ Microsoft SQL ▶ Oracle ▶ MySQL ▶ IBM DB2 ▶ Postgres ▶ Hadoop ▶ SQLite ▶ Any JDBC driver TAX MANAGEMENT ASSOCIATES, INC. KNIME DATABASE NODES TAX MANAGEMENT ASSOCIATES, INC. CORE DATA ANALYTICS FEATURES KNIME DATA ANALYTICS LIFECYCLE Read Data Extract, Data Analytics Reporting or Predictive Read Transform, and/or Load (ETL) Analysis Injection Data Read Data TAX MANAGEMENT ASSOCIATES, INC. -
List of NMAP Scripts Use with the Nmap –Script Option
List of NMAP Scripts Use with the nmap –script option Retrieves information from a listening acarsd daemon. Acarsd decodes ACARS (Aircraft Communication Addressing and Reporting System) data in real time. The information retrieved acarsd-info by this script includes the daemon version, API version, administrator e-mail address and listening frequency. Shows extra information about IPv6 addresses, such as address-info embedded MAC or IPv4 addresses when available. Performs password guessing against Apple Filing Protocol afp-brute (AFP). Attempts to get useful information about files from AFP afp-ls volumes. The output is intended to resemble the output of ls. Detects the Mac OS X AFP directory traversal vulnerability, afp-path-vuln CVE-2010-0533. Shows AFP server information. This information includes the server's hostname, IPv4 and IPv6 addresses, and hardware type afp-serverinfo (for example Macmini or MacBookPro). Shows AFP shares and ACLs. afp-showmount Retrieves the authentication scheme and realm of an AJP service ajp-auth (Apache JServ Protocol) that requires authentication. Performs brute force passwords auditing against the Apache JServ protocol. The Apache JServ Protocol is commonly used by ajp-brute web servers to communicate with back-end Java application server containers. Performs a HEAD or GET request against either the root directory or any optional directory of an Apache JServ Protocol ajp-headers server and returns the server response headers. Discovers which options are supported by the AJP (Apache JServ Protocol) server by sending an OPTIONS request and lists ajp-methods potentially risky methods. ajp-request Requests a URI over the Apache JServ Protocol and displays the result (or stores it in a file). -
Sheffield HPC Documentation
Sheffield HPC Documentation Release November 14, 2016 Contents 1 Research Computing Team 3 2 Research Software Engineering Team5 i ii Sheffield HPC Documentation, Release The current High Performance Computing (HPC) system at Sheffield, is the Iceberg cluster. A new system, ShARC (Sheffield Advanced Research Computer), is currently under development. It is not yet ready for use. Contents 1 Sheffield HPC Documentation, Release 2 Contents CHAPTER 1 Research Computing Team The research computing team are the team responsible for the iceberg service, as well as all other aspects of research computing. If you require support with iceberg, training or software for your workstations, the research computing team would be happy to help. Take a look at the Research Computing website or email research-it@sheffield.ac.uk. 3 Sheffield HPC Documentation, Release 4 Chapter 1. Research Computing Team CHAPTER 2 Research Software Engineering Team The Sheffield Research Software Engineering Team is an academically led group that collaborates closely with CiCS. They can assist with code optimisation, training and all aspects of High Performance Computing including GPU computing along with local, national, regional and cloud computing services. Take a look at the Research Software Engineering website or email rse@sheffield.ac.uk 2.1 Using the HPC Systems 2.1.1 Getting Started If you have not used a High Performance Computing (HPC) cluster, Linux or even a command line before this is the place to start. This guide will get you set up using iceberg in the easiest way that fits your requirements. Getting an Account Before you can start using iceberg you need to register for an account. -
D4.6 Real-Time Executor Software Release Y3
D4.6 Real-time Executor Software Release Y3 Grant agreement no. 780785 Project acronym OFERA (micro-ROS) Project full title Open Framework for Embedded Robot Applications Deliverable number D4.6 Deliverable name Real-time Executor – Software Release Y3 Date December 2020 Dissemination level public Workpackage and task 4.2 Author Jan Staschulat (Bosch) Contributors Ralph Lange (Bosch) Keywords micro-ROS, robotics, ROS, microcontrollers, scheduling, executor Abstract This document provides links to the released software and documentation for deliverable D4.6 Real-time Execu- tor Software Release Y3 of the Task 4.2 Predictable Scheduling and Execution. D4.6: Real-time Executor – Software Release Y3 Contents 1 Overview to Results 2 2 Links to Software Repositories 2 3 Annex 1: Webpage on Real-Time Executor 3 3.1 Table of contents ....................................... 3 3.2 Introduction ......................................... 4 3.3 Analysis of rclcpp standard Executor ........................... 5 3.3.1 Architecture ..................................... 5 3.3.2 Scheduling Semantics ................................ 6 3.4 RCLC-Executor ........................................ 7 3.4.1 Requirement Analysis ................................ 7 3.4.2 Features ........................................ 12 3.4.3 Executor API ..................................... 13 3.4.4 Examples ....................................... 14 3.4.5 Summary ....................................... 17 3.4.6 Future work ..................................... 18 3.4.7 Download -
Mathematica Document
Mathematica Project: Exploratory Data Analysis on ‘Data Scientists’ A big picture view of the state of data scientists and machine learning engineers. ����� ���� ��������� ��� ������ ���� ������ ������ ���� ������/ ������ � ���������� ���� ��� ������ ��� ���������������� �������� ������/ ����� ��������� ��� ���� ���������������� ����� ��������������� ��������� � ������(�������� ���������� ���������) ������ ��������� ����� ������� �������� ����� ������� ��� ������ ����������(���� �������) ��������� ����� ���� ������ ����� (���������� �������) ����������(���������� ������� ���������� ��� ���� ���� �����/ ��� �������������� � ����� ���� �� �������� � ��� ����/���������� ��������������� ������� ������������� ��� ���������� ����� �����(���� �������) ����������� ����� / ����� ��� ������ ��������������� ���������� ����������/�++ ������/������������/����/������ ���� ������� ����� ������� ������� ����������������� ������� ������� ����/����/�������/��/��� ����������(�����/����-�������� ��������) ������������ In this Mathematica project, we will explore the capabilities of Mathematica to better understand the state of data science enthusiasts. The dataset consisting of more than 10,000 rows is obtained from Kaggle, which is a result of ‘Kaggle Survey 2017’. We will explore various capabilities of Mathematica in Data Analysis and Data Visualizations. Further, we will utilize Machine Learning techniques to train models and Classify features with several algorithms, such as Nearest Neighbors, Random Forest. Dataset : https : // www.kaggle.com/kaggle/kaggle -
Direct Submission Or Co-Submission Direct Submission
Z-Matrix template-based substitution approach Title for enumeration of 3D molecular structures Authors Wanutcha Lorpaiboon and Taweetham Limpanuparb* Science Division, Mahidol University International College, Affiliations Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Corresponding Author’s email address [email protected] • Chemical structures • Education Keywords • Molecular generator • Structure generator • Z-matrix Direct Submission or Co-Submission Direct Submission ABSTRACT The exhaustive enumeration of 3D chemical structures based on Z-matrix templates has recently been used in the quantum chemical investigation of constitutional isomers, diastereomers and 5 rotamers. This simple yet powerful initial structure generation approach can apply beyond the investigation of compounds of identical formula by quantum chemical methods. This paper aims to provide a short description of the overall concept followed by a practical tutorial to the approach. • The four steps required for Z-matrix template-based substitution are template construction, generation of tuples for substitution sites, removal of duplicate tuples and 10 substitution on the template. • The generated tuples can be used to create chemical identifiers to query compound properties from chemical databases. • All of these steps are demonstrated in this paper by common model compounds and are very straightforward for an undergraduate audience to reproduce. A comparison of the 15 approach in this tutorial and other options is also discussed. SPECIFICATIONS TABLE Subject Area Chemistry More specific subject area Cheminformatics Method name Z-matrix template-based substitution Name and reference of original method N/A Source codes are available as supplementary information in this Resource availability paper. 2 of 10 Method details 20 1. Introduction Initial structures (Z-matrix or Cartesian coordinate) are important starting points for the in silico investigation of chemical species. -
Role of Materials Data Science and Informatics in Accelerated Materials Innovation Surya R
Role of materials data science and informatics in accelerated materials innovation Surya R. Kalidindi , David B. Brough , Shengyen Li , Ahmet Cecen , Aleksandr L. Blekh , Faical Yannick P. Congo , and Carelyn Campbell The goal of the Materials Genome Initiative is to substantially reduce the time and cost of materials design and deployment. Achieving this goal requires taking advantage of the recent advances in data and information sciences. This critical need has impelled the emergence of a new discipline, called materials data science and informatics. This emerging new discipline not only has to address the core scientifi c/technological challenges related to datafi cation of materials science and engineering, but also, a number of equally important challenges around data-driven transformation of the current culture, practices, and workfl ows employed for materials innovation. A comprehensive effort that addresses both of these aspects in a synergistic manner is likely to succeed in realizing the vision of scaled-up materials innovation. Key toolsets needed for the successful adoption of materials data science and informatics in materials innovation are identifi ed and discussed in this article. Prototypical examples of emerging novel toolsets and their functionality are described along with select case studies. Introduction goal of reducing the time and cost of materials development Materials innovation initiatives and deployment by 50%. 1 Essential to achieving this goal is A number of US-based, 1 – 3 as well as international, 4 , 5 efforts are the development and deployment of a supporting infrastruc- now focused on accelerated deployment of advanced materials ture that integrates a wide range of data, experimental, and in commercial products. -
Titel Untertitel
KNIME Image Processing Nycomed Chair for Bioinformatics and Information Mining Department of Computer and Information Science Konstanz University, Germany Why Image Processing with KNIME? KNIME UGM 2013 2 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … = Single, individual, case specific, incompatible solutions KNIME UGM 2013 3 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … → Integration! KNIME UGM 2013 4 KNIME as integration platform KNIME UGM 2013 5 Integration: What and How? KNIME UGM 2013 6 Integration ImgLib2 • Developed at MPI-CBG Dresden • Generic framework for data (image) processing algoritms and data-structures • Generic design of algorithms for n-dimensional images and labelings • http://fiji.sc/wiki/index.php/ImgLib2 → KNIME: used as image representation (within the data cells); basis for algorithms KNIME UGM 2013 7 Integration ImageJ/Fiji • Popular, highly interactive image processing tool • Huge base of available plugins • Fiji: Extension of ImageJ1 with plugin-update mechanism and plugins • http://rsb.info.nih.gov/ij/ & http://fiji.sc/ → KNIME: ImageJ Macro Node KNIME UGM 2013 8 Integration ImageJ2 • Next-generation version of ImageJ • Complete re-design of ImageJ while maintaining backwards compatibility • Based on ImgLib2